某学校选课系统GIF验证码数据集 + Baseline模型 + 上下游相关工具

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Deep Learningelective-dataset-2021spring
Overview

elective-dataset-2021spring

某学校2021春季选课系统GIF验证码数据集（29338张） + 准确率98.4%的Baseline模型 + 上下游相关工具。

数据集采用 知识共享署名-非商业性使用 4.0 国际许可协议 进行许可。

Baseline模型和上下游相关工具采用 MIT License 进行许可。

数据集

dataset/ 目录包含了收集到的所有带标签验证码数据，共29338张。

  • dataset/manual： 人工标注的带标签验证码GIF数据集，标签经过了elective验证因此都是正确的。共5471张。
  • dataset/auto-corr、dataset/auto-fail-tagged： 模型自动标注的带标签验证码GIF数据集，其中 auto-corr 是识别正确（通过了elective验证）的部分，auto-fail-tagged 是识别错误然后手工重新标注的部分（此部分不保证正确性）。共22931（正确）+936（错误）张。

使用时请注意，由于 GitHub 的限制：

  • auto-fail-tagged 在仓库中存储为7-zip压缩包；
  • manual 在仓库中存储为7个不超过48MB的7-zip分卷；
  • auto-corr 没有存储在仓库中，而是压缩为14个不超过95MB的7-zip分卷放在了 Release页面。

Baseline 模型

baseline/ 目录包含一个简易的验证码识别模型。

此模型进行了提取关键帧、基于OpenCV的图像增强以及基于CNN的分类器等一系列工作以完成识别。

将训练集和测试集图片分别放入 set-train 和 set-test 后运行 train.py 进行训练，用一块TITAN RTX训练需要几分钟的时间。

用大约一万张图片训练好的 checkpoints/model_29.pth 能达到 98.4% 的整体精确度。

predict_bootstrap.py 在elective系统上测试当前模型，将检验正确的带标签图片放入 bootstrap_img_succ 目录，错误的图片放入 bootstrap_img_fail 目录。

上下游相关工具

  • crawl/： 验证码众包标注平台，可以从elective爬取验证码、辅助多名用户同时标注、检验正确性后将正确的数据放入 img_correct 目录。检验错误的验证码将被抛弃，这是初期的一个设计失误，这样将使得数据集的分布与真实分布有偏差。
  • retag/： 手工标注模型识别错误数据的工具。从 bootstrap_img_fail 读取标注错误图片，人工输入正确标注后移动到 bootstrap_img_fail_tagged。
  • serve/： 提供在线验证码识别服务的 HTTP RPC 服务器。POST /fire 并传入base64编码的验证码GIF来进行识别。

数据处理过程

首先，我们设立了众包标注平台，多名志愿者累计标注了超过五千张验证码。

有了这些数据后，我们利用OpenCV进行了简单的图片增强、二值化、分字、裁切，然后随手糊了一个简单的CNN网络来识别。在随意调参之后，模型的整体（四个字）准确率接近95%。

然后，我们利用此模型来对数据集进行自举：爬取验证码后调用模型识别然后检验正确性，其中识别错误的部分手工标注。这样我们可以轻易地扩大数据集的规模，从而提升模型效果。

经过了更多的随意调参，模型的整体准确率可以达到98.4%。因为继续提升准确率意义不大，就没有继续优化。考虑到 PyTorch 安装比较麻烦，模型不易于部署到用户的设备上，我们实现了一个 HTTP API 可以用于云端识别。

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by Elector Quartet (按字典序的倒序 @xmcp, @SpiritedAwayCN, @Rabbit, @gzz)

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